Summary of Erasing the Bias: Fine-tuning Foundation Models For Semi-supervised Learning, by Kai Gan et al.
Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
by Kai Gan, Tong Wei
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents FineSSL, a novel semi-supervised learning (SSL) approach that addresses the limitations of existing method variations. By adapting pre-trained foundation models, FineSSL imposes balanced margin softmax and decoupled label smoothing to mitigate aggregated biases and cognitive deviation problems. The proposed solution sets a new state-of-the-art for SSL on multiple benchmark datasets, reduces training cost by over six times, and integrates seamlessly with various fine-tuning and modern SSL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new way to learn from both labeled and unlabeled data. They call it FineSSL. It’s better than other methods because it uses special tricks to make the models work well. The tricks are called balanced margin softmax and decoupled label smoothing. This helps the model learn more accurately and efficiently. They tested FineSSL on many different datasets and showed that it works really well. Plus, it can be used with other learning techniques. |
Keywords
» Artificial intelligence » Fine tuning » Semi supervised » Softmax
